The literature on matched pairs with missing data has primarily focused on one-dimensional, continuous, discrete or ordinal variables, aimed at detecting changes in location/mean [1]}, [2]}, [3]}, [4]}, scale/variance [5]}, and distribution [6]}. Some of the proposals apply multiple imputation techniques [7]}, [8]}, [9]}, but they often require large sample sizes for being correct. Other proposals rely on specific model assumptions such as symmetry or bivariate normality [1]}, [11]}, [4]}, but they exhibit a non-robust behavior against deviations. The common approach recently adopted in literature results from combining in a non parametric approach separate test statistics for the paired and unpaired observations, by using either weighted test statistics [13]}, [14]}, [6]}, [16]}, [3]}, [18]}, a multiplication combination test [19]}, or combined p-values [20]}, [21]}, [22]}, [23]}
